AI Systems Engineer Graduate (AI Code Infrastructure-Agentic Ops US) - 2027 Start

The Agentic Ops US team focuses on two major areas: Code Graph and Quality Validation. The code graph serves as the data foundation for validation, encompassing static relationships (function calls, experiment/instrumentation dependencies) and dynamic execution paths (reconstructed from traces). Quality validation covers static rule checking (mining soft and logical constraints) and dynamic issue detection, reproduction, and fixing. We aim to improve code reliability and R&D efficiency through systematic, data-driven approaches., * Participate in building and optimizing the code graph, including static relationship extraction, dynamic trace data cleansing, and aggregation.

• Develop static validation rules (logical and soft constraints) based on the graph, and contribute to the design and implementation of dynamic validation tools (issue reproduction, root cause localization).
• Conduct in-depth analysis of production quality issues, distill general validation patterns, and drive automation coverage.
• Research and introduce cutting-edge academic and industrial techniques in code analysis, program slicing, anomaly detection, etc., to continuously improve validation effectiveness. Minimum Qualifications

Systems Engineering, Artificial Intelligence (AI), Research & Development (R&D), Instrumentation, Reliability Engineering, Onboarding, Data Collection, Localization, Automation, Problem Solving Skills, * Individuals who are completing or have recently completed a Bachelor s degree in computer science or a related discipline.

• Solid programming skills with proficiency in at least one mainstream language (Go / Python / Java / C++) and good coding practices.
• Deep understanding of data structures and algorithms, capable of independently reading and implementing moderately complex algorithm modules.
• Research-oriented mindset: curious about unknown problems, adept at reading papers and technical blogs, and able to design solutions from first principles rather than just making code work.
• Strong learning agility: able to quickly pick up new tools and frameworks, proactively track technology evolution, and apply them to practical work., * Experience with static analysis (e.g., AST, CFG, pointer analysis), dynamic tracing (e.g., eBPF, OpenTelemetry), compiler development, or automated testing.
• Publications in academic conferences or journals related to code analysis, software engineering, or system reliability.
• Contributions to major open-source projects, or personal tech blog/portfolio that demonstrates independent thinking and deep-dive investigation.